Managing internode data communications for an uninitialized process in a parallel computer

ABSTRACT

A parallel computer includes nodes, each having main memory and a messaging unit (MU). Each MU includes computer memory, which in turn includes, MU message buffers. Each MU message buffer is associated with an uninitialized process on the compute node. In the parallel computer, managing internode data communications for an uninitialized process includes: receiving, by an MU of a compute node, one or more data communications messages in an MU message buffer associated with an uninitialized process on the compute node; determining, by an application agent, that the MU message buffer associated with the uninitialized process is full prior to initialization of the uninitialized process; establishing, by the application agent, a temporary message buffer for the uninitialized process in main computer memory; and moving, by the application agent, data communications messages from the MU message buffer associated with the uninitialized process to the temporary message buffer in main computer memory.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of and claims priorityfrom U.S. patent application Ser. No. 13/292,293, filed on Nov. 9, 2011.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Contract No.B554331 awarded by the Department of Energy. The Government has certainrights in this invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for managing internode datacommunications for an uninitialized process in a parallel computer.

2. Description of Related Art

The development of the Electronic Discrete Variable Automatic Computer(‘EDVAC’) computer system of 1948 is often cited as the beginning of thecomputer era. Since that time, computer systems have evolved intoextremely complicated devices. Today's computers are much moresophisticated than early systems such as the EDVAC. Computer systemstypically include a combination of hardware and software components,application programs, operating systems, processors, buses, memory,input/output devices, and so on. As advances in semiconductor processingand computer architecture push the performance of the computer higherand higher, more sophisticated computer software has evolved to takeadvantage of the higher performance of the hardware, resulting incomputer systems today that are much more powerful than just a few yearsago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same application (split up and specially adapted) on multipleprocessors in order to obtain results faster. Parallel computing isbased on the fact that the process of solving a problem usually can bedivided into smaller jobs, which may be carried out simultaneously withsome coordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing jobs via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x, y, z coordinate in the mesh. In atree network, the nodes typically are connected into a binary tree: eachnode has a parent and two children (although some nodes may only havezero children or one child, depending on the hardware configuration). Incomputers that use a torus and a tree network, the two networkstypically are implemented independently of one another, with separaterouting circuits, separate physical links, and separate message buffers.

A torus network lends itself to point to point operations, but a treenetwork typically is inefficient in point to point communication. A treenetwork, however, does provide high bandwidth and low latency forcertain collective operations, message passing operations where allcompute nodes participate simultaneously, such as, for example, anallgather.

SUMMARY OF THE INVENTION

In a multi-node distributed processing system that supports sending datacommunications messages to an uninitialized process, a buffer local tothe node and configured to receive such messages may fill prior toinitialization of that process. Once the local buffer is full,subsequent data communication messages sent to the uninitialized processmay cause network congestion, effectively backing up network bufferswith message data that cannot be delivered until the process isinitialized.

To that end, this specification sets forth methods, apparatus, andproducts managing internode data communications for an uninitializedprocess in a parallel computer. The parallel computer includes aplurality of compute nodes, with each compute node comprising maincomputer memory and a messaging unit (MU). Each MU may be implemented asa module of automated computing machinery coupling compute nodes fordata communications. Each MU includes computer memory which, in turn,includes one or more MU message buffers. Each MU message buffer isassociated with an uninitialized process on the compute node. Inembodiments of the present invention, internode data communications foran uninitialized process are managed by: receiving, by an MU of acompute node, one or more data communications messages in an MU messagebuffer associated with an uninitialized process on the compute node;determining, by an application agent, that the MU message bufferassociated with the uninitialized process is full prior toinitialization of the uninitialized process; establishing, by theapplication agent, a temporary message buffer for the uninitializedprocess in main computer memory; and moving, by the application agent,data communications messages from the MU message buffer associated withthe uninitialized process to the temporary message buffer in maincomputer memory.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 sets forth a block and network diagram of an example parallelcomputer that manages internode data communications for an uninitializedprocess according to embodiments of the present invention.

FIG. 2 sets forth a block diagram of an example compute node for use ina parallel computer that manages internode data communications for anuninitialized process according to embodiments of the present invention.

FIG. 3A illustrates an example of a Point To Point Adapter useful inparallel computers that manages internode data communications for anuninitialized process according to embodiments of the present invention.

FIG. 3B illustrates an example of a Collective Operations Adapter usefulin a parallel computer that manages internode data communications for anuninitialized process computer according to embodiments of the presentinvention.

FIG. 4 sets forth a line drawing illustrating an example datacommunications network optimized for point-to-point operations useful inparallel computers that manages internode data communications for anuninitialized process computer according to embodiments of the presentinvention.

FIG. 5 illustrates an example data communications network optimized forcollective operations by organizing compute nodes in a tree.

FIG. 6 sets forth a block diagram of an example protocol stack useful inparallel computers that manages internode data communications for anuninitialized process according to embodiments of the present invention.

FIG. 7 sets forth a functional block diagram of an example PAMI for usein parallel computers that manages internode data communications for anuninitialized process according to embodiments of the present invention.

FIG. 8A sets forth a block diagram of example data communicationsresources useful in parallel computers that manages internode datacommunications for an uninitialized process according to embodiments ofthe present invention.

FIG. 8B sets forth a functional block diagram of an example DMAcontroller operatively coupled to a network—in an architecture wherethis DMA controller is the only DMA controller on a compute node—and anorigin endpoint and its target endpoint are both located on the samecompute node.

FIG. 9 sets forth a functional block diagram of an example PAMI usefulin parallel computers that manages internode data communications for anuninitialized process according to embodiments of the present inventionin which the example PAMI operates, on behalf of an application, withmultiple application messaging modules simultaneously.

FIG. 10 sets forth a functional block diagram of example endpointsuseful in parallel computers that manage internode data communicationsfor an uninitialized process according to embodiments of the presentinvention.

FIG. 11 sets forth a flow chart illustrating an example method ofmanaging internode data communications for an uninitialized process in aparallel computer according to embodiments of the present invention.

FIG. 12 sets forth a flow chart illustrating a further example method ofmanaging internode data communications for an uninitialized process in aparallel computer according to embodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Example methods, computers, and computer program products for managinginternode data communications for an uninitialized process in a parallelcomputer according to embodiments of the present invention are describedwith reference to the accompanying drawings, beginning with FIG. 1. FIG.1 sets forth a block and network diagram of an example parallel computer(100) that manages internode data communications for an uninitializedprocess according to embodiments of the present invention. The parallelcomputer (100) in the example of FIG. 1 is coupled to non-volatilememory for the computer in the form of data storage device (118), anoutput device for the computer in the form of printer (120), and aninput/output device for the computer in the form of computer terminal(122).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a tree network (106) which isoptimized for collective operations, and a torus network (108) which isoptimized point to point operations. Tree network (106) is a datacommunications network that includes data communications links connectedto the compute nodes so as to organize the compute nodes as a tree. Eachdata communications network is implemented with data communicationslinks among the compute nodes (102). The data communications linksprovide data communications for parallel operations among the computenodes of the parallel computer.

In addition, the compute nodes (102) of parallel computer (100) areorganized into at least one operational group (132) of compute nodes forcollective parallel operations on parallel computer (100). Anoperational group of compute nodes is the set of compute nodes uponwhich a collective parallel operation executes. Collective operationsare implemented with data communications among the compute nodes of anoperational group. Collective operations are those functions thatinvolve all the compute nodes of an operational group. A collectiveoperation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group ofcompute nodes. Such an operational group may include all the computenodes in a parallel computer (100) or a subset all the compute nodes.Collective operations are often built around point to point operations.A collective operation requires that all processes on all compute nodeswithin an operational group call the same collective operation withmatching arguments. A ‘broadcast’ is an example of a collectiveoperation for moving data among compute nodes of an operational group. A‘reduce’ operation is an example of a collective operation that executesarithmetic or logical functions on data distributed among the computenodes of an operational group. An operational group may be implementedas, for example, an MPI ‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art applicationsmessaging module or parallel communications library, anapplication-level messaging module of computer program instructions fordata communications on parallel computers. Such an application messagingmodule is disposed in an application messaging layer in a datacommunications protocol stack. Examples of prior-art parallelcommunications libraries that may be improved for use with parallelcomputers that manage internode data communications for an uninitializedprocess according to embodiments of the present invention include IBM'sMPI library, the ‘Parallel Virtual Machine’ (‘PVM’) library, MPICH,OpenMPI, and LAM/MPI. MPI is promulgated by the MPI Forum, an open groupwith representatives from many organizations that define and maintainthe MPI standard. MPI at the time of this writing is a de facto standardfor communication among compute nodes running a parallel program on adistributed memory parallel computer. This specification sometimes usesMPI terminology for ease of explanation, although the use of MPI as suchis not a requirement or limitation of the present invention.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. In a broadcastoperation, all processes specify the same root process, whose buffercontents will be sent. Processes other than the root specify receivebuffers. After the operation, all buffers contain the message from theroot process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. All processes specify the same receive count. Thesend arguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer will be divided equally and dispersed from the root to allprocesses (including the root). Each process is assigned a sequentialidentifier termed a ‘rank.’ After the operation, the root has sentsendcount data elements to each process in increasing rank order. Rank 0(the root process) receives the first sendcount data elements from thesend buffer. Rank 1 receives the second sendcount data elements from thesend buffer, and so on.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked processes into a receive buffer ofthe root process.

A reduce operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from compute node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process's receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following predefinedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical AND MPI_BAND bitwise AND MPI_LOR logical OR MPI_BOR bitwise ORMPI_LXOR logical exclusive OR MPI_BXOR bitwise exclusive OR

In addition to compute nodes, the example parallel computer (100)includes input/output (‘I/O’) nodes (110, 114) coupled to compute nodes(102) through one of the data communications networks (174). The I/Onodes (110, 114) provide I/O services between compute nodes (102) andI/O devices (118, 120, 122). I/O nodes (110, 114) are connected for datacommunications I/O devices (118, 120, 122) through local area network(‘LAN’) (130). Computer (100) also includes a service node (116) coupledto the compute nodes through one of the networks (104). Service node(116) provides service common to pluralities of compute nodes, loadingprograms into the compute nodes, starting program execution on thecompute nodes, retrieving results of program operations on the computenodes, and so on. Service node (116) runs a service application (124)and communicates with users (128) through a service applicationinterface (126) that runs on computer terminal (122).

As the term is used here, a parallel active messaging interface or‘PAMI’ (218) is a system-level messaging layer in a protocol stack of aparallel computer that is composed of data communications endpoints eachof which is specified with data communications parameters for a threadof execution on a compute node of the parallel computer. The PAMI is a‘parallel’ interface in that many instances of the PAMI operate inparallel on the compute nodes of a parallel computer. The PAMI is an‘active messaging interface’ in that data communications messages in thePAMI are active messages, ‘active’ in the sense that such messagesimplement callback functions to advise of message dispatch andinstruction completion and so on, thereby reducing the quantity ofacknowledgment traffic, and the like, burdening the data communicationresources of the PAMI.

Each data communications endpoint of a PAMI is implemented as acombination of a client, a context, and a task. A ‘client’ as the termis used in PAMI operations is a collection of data communicationsresources dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. A ‘context’ as the term is used in PAMIoperations is composed of a subset of a client's collection of dataprocessing resources, context functions, and a work queue of datatransfer instructions to be performed by use of the subset through thecontext functions operated by an assigned thread of execution. In atleast some embodiments, the context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context. A‘task’ as the term is used in PAMI operations refers to a canonicalentity, an integer or objection oriented programming object, thatrepresents in a PAMI a process of execution of the parallel application.That is, a task is typically implemented as an identifier of aparticular instance of an application executing on a compute node, acompute core on a compute node, or a thread of execution on amulti-threading compute core on a compute node.

In the example of FIG. 1, the compute nodes (102), as well as PAMIendpoints on the compute nodes, are coupled for data communicationsthrough the PAMI (218) and through data communications resources such ascollective network (106) and point-to-point network (108).

The example parallel computer (100) of FIG. 1 is improved to manageinternode data communications for an uninitialized process according toembodiments of the present invention. Each compute node (102) in theexample of FIG. 1 is configured to execute a plurality of processes.Such a process may be a process in PAMI (a PAMI endpoint, for example),a process representing an instance of an application, or other type ofprocess. Each compute node also includes main computer memory (156) anda messaging unit. The messaging unit, sometimes implemented as a DMAcontroller, includes computer memory (138) and is a module of automatedcomputing machinery that couples compute nodes for data communications.

At boot time of each compute node (102) in the example parallel computer(100) of FIG. 1, the messaging unit allocates, in the messaging unit'scomputer memory (138), a predefined number of MU message buffers (140).Each MU message buffer is associated with a process to be initialized onthe compute node. Each MU message buffer may include a reception FIFO(first-in, first-out) buffer and an injection FIFO buffer for a process.The MU message buffers (140) are established without regard to whetherthe process associated with the message buffer has actually beeninitialized. In some embodiments, each MU message buffer is of apredefined size. Consider, for example, that MU memory consists of a 128megabytes (MB) and no more than 64 process may be initialized on acompute node at any given time. The MU may establish 64 message buffersof 2 MB each.

The messaging unit is configured to receive, prior to initialization ofa process on the compute node (102), a data communications message (146)intended for the process. The data communications message is receivedfrom a process executing on a different compute node. The messaging unitstores the data communications message (146) in the MU message buffer(140) associated with the uninitialized process and, upon initializationof the process, the process establishes a messaging buffer in mainmemory (156) of the compute node and copies the data communicationsmessage (146) from the MU message buffer (140) into the main memorymessage buffer of main memory (156). In this way, processes may senddata to another process on another compute node regardless of whetherthe process on the other compute node has been initialized.

In some instances, however the MU message buffer may fill prior toinitialization of the uninitialized process. To that end, each computenode (102) executes an application agent (134) configured to determine,by an application agent, that the MU message buffer associated with theuninitialized process is full prior to initialization of theuninitialized process. Upon such a determination, the application agent(134) establishes a temporary message buffer (136) for the uninitializedprocess in main computer memory (156) and moves data communicationsmessages (146) from the MU message buffer (140) associated with theuninitialized process to the temporary message buffer (136) in maincomputer memory (156). The application agent is said to ‘move’ datacommunications messages in that the messages are copied to the temporarymessage buffer and then removed from the MU message buffer—free the MUmessage buffer to receive subsequent data communications messages.

The message buffer (136) established by the application agent isdescribed here as ‘temporary’ in that the message buffer will beprocessed and freed upon initialization of the process. Specifically,upon initialization of the uninitialized process, the application agent(134) provides to the initialized process a pointer to the temporarymessage buffer and consuming, by the initialized process in dependenceupon the pointer, data communications messages from the temporarymessage buffer prior to consuming data communications messages stored inthe MU message buffer. In this way, the application agent effectivelyexpands available message storage on-demand, for processes yet to fullyinitialize.

In some embodiments, a process may never initialize, through a softwareor hardware error for example. In such an embodiment, the system of FIG.1 may be configured to ensure delivery of the data communicationsmessages. To ensure completed delivery of data communications in such asystem, a sending process may be configured to periodically poll, for apredefined amount of time, the target process's message buffer todetermine whether the data communications message has been retrieved bythe target process. If the data communications message has not beenretrieved during the predefined amount of time, the sending process mayflush the target process's message buffer (overwriting the datacommunications message) and send the data communications message toanother process. Readers of skill in the art will recognize that pollingthe target process's message buffer for a predefined amount of time isbut one way, among many possible ways, to ensure delivery of a datacommunications message to an uninitialized process. In another example,processes may be configured to send acknowledgments of receipt of datacommunications messages. In such an example, a sending process may beconfigured to send the data communications message to a target process,wait for acknowledgement from the target process for a predefined amountof time, and send the data communications message to another process ifthe sending process has not received an acknowledgement from the targetprocess after the predefined amount of time.

The arrangement of compute nodes, networks, and I/O devices making upthe example parallel computer illustrated in FIG. 1 are for explanationonly, not for limitation of the present invention. Parallel computerscapable of managing internode data communications for an uninitializedprocess according to embodiments of the present invention may includeadditional nodes, networks, devices, and architectures, not shown inFIG. 1, as will occur to those of skill in the art. Readers willrecognize that compute nodes in parallel computers that manage internodedata communications for an uninitialized process according toembodiments of the present invention can include any number ofprocessors as may occur to those of skill in the art; each compute nodein IBM's BlueGene/Q supercomputer, for example, includes 16 applicationprocessors and a management processor. The parallel computer (100) inthe example of FIG. 1 includes sixteen compute nodes (102); parallelcomputers that manage internode data communications for an uninitializedprocess according to some embodiments of the present invention includethousands of compute nodes. In addition to Ethernet and JTAG, networksin such data processing systems may support many data communicationsprotocols including for example TCP (Transmission Control Protocol), IP(Internet Protocol), and others as will occur to those of skill in theart. Various embodiments of the present invention may be implemented ona variety of hardware platforms in addition to those illustrated in FIG.1.

Managing internode data communications for an uninitialized process in aparallel computer according to embodiments of the present invention isgenerally implemented on a parallel computer that includes a pluralityof compute nodes. In fact, such computers may include thousands of suchcompute nodes, with a compute node typically executing at least oneinstance of a parallel application. Each compute node is in turn itselfa computer composed of one or more computer processors, its own computermemory, and its own input/output (‘I/O’) adapters. For furtherexplanation, therefore, FIG. 2 sets forth a block diagram of an examplecompute node (152) for use in a parallel computer that manages internodedata communications for an uninitialized process according toembodiments of the present invention. The compute node (152) of FIG. 2includes one or more computer processors (164) as well as random accessmemory (‘RAM’) (156). Each processor (164) can support multiple hardwarecompute cores (165), and each such core can in turn support multiplethreads of execution, hardware threads of execution as well as softwarethreads. Each processor (164) is connected to RAM (156) through ahigh-speed front side bus (161), bus adapter (194), and a high-speedmemory bus (154)—and through bus adapter (194) and an extension bus(168) to other components of the compute node. Stored in RAM (156) is anapplication program (158), a module of computer program instructionsthat carries out parallel, user-level data processing using parallelalgorithms.

Also stored RAM (156) is an application messaging module (216), alibrary of computer program instructions that carry outapplication-level parallel communications among compute nodes, includingpoint to point operations as well as collective operations. Although theapplication program can call PAMI routines directly, the applicationprogram (158) often executes point-to-point data communicationsoperations by calling software routines in the application messagingmodule (216), which in turn is improved according to embodiments of thepresent invention to use PAMI functions to implement suchcommunications. An application messaging module can be developed fromscratch to use a PAMI according to embodiments of the present invention,using a traditional programming language such as the C programminglanguage or C++, for example, and using traditional programming methodsto write parallel communications routines that send and receive dataamong PAMI endpoints and compute nodes through data communicationsnetworks or shared-memory transfers. In this approach, the applicationmessaging module (216) exposes a traditional interface, such as MPI, tothe application program (158) so that the application program can gainthe benefits of a PAMI with no need to recode the application. As analternative to coding from scratch, therefore, existing prior artapplication messaging modules may be improved to use the PAMI, existingmodules that already implement a traditional interface. Examples ofprior-art application messaging modules that can be improved to manageinternode data communications for an uninitialized process in a parallelcomputer according to embodiments of the present invention include suchparallel communications libraries as the traditional ‘Message PassingInterface’ (‘MPI’) library, the ‘Parallel Virtual Machine’ (‘PVM’)library, MPICH, and the like.

Also represented in RAM in the example of FIG. 2 is a PAMI (218).Readers will recognize, however, that the representation of the PAMI inRAM is a convention for ease of explanation rather than a limitation ofthe present invention, because the PAMI and its components, endpoints,clients, contexts, and so on, have particular associations with andinclusions of hardware data communications resources. In fact, the PAMIcan be implemented partly as software or firmware and hardware—or even,at least in some embodiments, entirely in hardware.

Also represented in RAM (156) in the example of FIG. 2 is a segment(227) of shared memory. In typical operation, the operating system (162)in this example compute node assigns portions of address space to eachprocessor (164), and, to the extent that the processors include multiplecompute cores (165), treats each compute core as a separate processorwith its own assignment of a portion of core memory or RAM (156) for aseparate heap, stack, memory variable storage, and so on. The defaultarchitecture for such apportionment of memory space is that eachprocessor or compute core operates its assigned portion of memoryseparately, with no ability to access memory assigned to anotherprocessor or compute core. Upon request, however, the operating systemgrants to one processor or compute core the ability to access a segmentof memory that is assigned to another processor or compute core, andsuch a segment is referred to in this specification as a ‘segment ofshared memory.’

In the example of FIG. 2, each processor or compute core has uniformaccess to the RAM (156) on the compute node, so that accessing a segmentof shared memory is equally fast regardless where the shared segment islocated in physical memory. In some embodiments, however, modules ofphysical memory are dedicated to particular processors, so that aprocessor may access local memory quickly and remote memory more slowly,a configuration referred to as a Non-Uniform Memory Access or ‘NUMA.’ Insuch embodiments, a segment of shared memory can be configured locallyfor one endpoint and remotely for another endpoint—or remotely from bothendpoints of a communication. From the perspective of an origin endpointtransmitting data through a segment of shared memory that is configuredremotely with respect to the origin endpoint, transmitting data throughthe segment of shared memory will appear slower that if the segment ofshared memory were configured locally with respect to the originendpoint—or if the segment were local to both the origin endpoint andthe target endpoint. This is the effect of the architecture representedby the compute node (152) in the example of FIG. 2 with all processorsand all compute cores coupled through the same bus to the RAM—that allaccesses to segments of memory shared among processes or processors onthe compute node are local—and therefore very fast.

The example compute node (152) of FIG. 2 is also configured with amessaging unit—implemented in this example as a DMA controller (225).The DMA controller (225) is coupled to messaging unit memory (138). Insome embodiments, the MU memory (138) is of a predefined size, muchsmaller than main memory. That is, memory resources available to the DMAcontroller may be sparse in some embodiments. The DMA controller (225)supports management of internode data communications for anuninitialized process in a parallel computer in accordance withembodiments of the present invention by: allocating, at boot time of thecompute node (152) in the MU memory (138), a predefined number of MUmessage buffers (140), where each MU message buffer is associated withan uninitialized process on the compute node. The DMA controller (225)may receive, prior to initialization of a process on the compute node(152), a data communications message (146) intended for the process andmay store the data communications message in the MU message buffer (140)associated with the uninitialized process.

As data communications messages intended for the uninitialized processare received, the MU message buffer (140), being of a predefined,limited size, may fill. To that end, the compute node (152) executes anapplication agent (134). The application agent (134) may determine thatthe MU message buffer (140) associated with the uninitialized process isfull prior to initialization of the uninitialized process; establish atemporary message buffer (136) for the uninitialized process in maincomputer memory (156); and move data communications messages (146) fromthe MU message buffer (140) associated with the uninitialized process tothe temporary message buffer (136) in main computer memory.

In some embodiments, the application agent is configured to execute onone core (165) of the processors (164), while no other applicationexecutes on the same core. In this way, a portion of the shared memorysegment (227) may be designated to that core and the application agent'suse.

Also stored in RAM (156) in the example compute node of FIG. 2 is anoperating system (162), a module of computer program instructions androutines for an application program's access to other resources of thecompute node. It is possible, in some embodiments at least, for anapplication program, an application messaging module, and a PAMI in acompute node of a parallel computer to run threads of execution with nouser login and no security issues because each such thread is entitledto complete access to all resources of the node. The quantity andcomplexity of duties to be performed by an operating system on a computenode in a parallel computer therefore can be somewhat smaller and lesscomplex than those of an operating system on a serial computer with manythreads running simultaneously with various level of authorization foraccess to resources. In addition, there is no video I/O on the computenode (152) of FIG. 2, another factor that decreases the demands on theoperating system. The operating system may therefore be quitelightweight by comparison with operating systems of general purposecomputers, a pared down or ‘lightweight’ version as it were, or anoperating system developed specifically for operations on a particularparallel computer. Operating systems that may be improved or simplifiedfor use in a compute node according to embodiments of the presentinvention include UNIX™, Linux™, Microsoft XP™, AIX™, IBM's i5/OS™, andothers as will occur to those of skill in the art.

The example compute node (152) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters for use in computers that manage internode datacommunications for an uninitialized process according to embodiments ofthe present invention include modems for wired communications, Ethernet(IEEE 802.3) adapters for wired network communications, and 802.11badapters for wireless network communications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (152)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 includes aJTAG Slave circuit (176) that couples example compute node (152) fordata communications to a JTAG Master circuit (178). JTAG is the usualname for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also used as amechanism for debugging embedded systems. The example compute node ofFIG. 2 may be all three of these: It typically includes one or moreintegrated circuits installed on a printed circuit board and may beimplemented as an embedded system having its own processor, its ownmemory, and its own I/O capability. JTAG boundary scans through JTAGSlave (176) may efficiently configure processor registers and memory incompute node (152) for use in managing internode data communications foran uninitialized process according to embodiments of the presentinvention.

The data communications adapters in the example of FIG. 2 includes aPoint To Point Adapter (180) that couples example compute node (152) fordata communications to a data communications network (108) that isoptimal for point to point message passing operations such as, forexample, a network configured as a three-dimensional torus or mesh.Point To Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186). For ease of explanation, the Point To Point Adapter (180)of FIG. 2 as illustrated is configured for data communications in threedimensions, x, y, and z, but readers will recognize that Point To PointAdapters optimized for point-to-point operations in a parallel computerthat manages internode data communications for an uninitialized processaccording to embodiments of the present invention may in fact beimplemented so as to support communications in two dimensions, fourdimensions, five dimensions, and so on.

The data communications adapters in the example of FIG. 2 includes aCollective Operations Adapter (188) that couples example compute node(152) for data communications to a network (106) that is optimal forcollective message passing operations such as, for example, a networkconfigured as a binary tree. Collective Operations Adapter (188)provides data communications through three bidirectional links: two tochildren nodes (190) and one to a parent node (192).

The example compute node (152) includes a number of arithmetic logicunits (‘ALUs’). ALUs (166) are components of processors (164), and aseparate ALU (170) is dedicated to the exclusive use of collectiveoperations adapter (188) for use in performing the arithmetic andlogical functions of reduction operations. Computer program instructionsof a reduction routine in an application messaging module (216) or aPAMI (218) may latch an instruction for an arithmetic or logicalfunction into instruction register (169). When the arithmetic or logicalfunction of a reduction operation is a ‘sum’ or a ‘logical OR,’ forexample, collective operations adapter (188) may execute the arithmeticor logical operation by use of an ALU (166) in a processor (164) or,typically much faster, by use of the dedicated ALU (170).

The example compute node (152) of FIG. 2 includes a direct memory access(‘DMA’) controller (225), a module of automated computing machinery thatimplements, through communications with other DMA engines on othercompute nodes, or on a same compute node, direct memory access to andfrom memory on its own compute node as well as memory on other computenodes. Direct memory access is a way of reading and writing to and frommemory of compute nodes with reduced operational burden on computerprocessors (164); a CPU initiates a DMA transfer, but the CPU does notexecute the DMA transfer. A DMA transfer essentially copies a block ofmemory from one compute node to another, or between RAM segments ofapplications on the same compute node, from an origin to a target for aPUT operation, from a target to an origin for a GET operation.

For further explanation, FIG. 3A illustrates an example of a Point ToPoint Adapter (180) useful in parallel computers that manage internodedata communications for an uninitialized process according toembodiments of the present invention. Point To Point Adapter (180) isdesigned for use in a data communications network optimized for point topoint operations, a network that organizes compute nodes in athree-dimensional torus or mesh. Point To Point Adapter (180) in theexample of FIG. 3A provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). Point To Point Adapter (180) also provides datacommunication along a y-axis through four unidirectional datacommunications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). Point ToPoint Adapter (180) in also provides data communication along a z-axisthrough four unidirectional data communications links, to and from thenext node in the −z direction (186) and to and from the next node in the+z direction (185). For ease of explanation, the Point To Point Adapter(180) of FIG. 3A as illustrated is configured for data communications inonly three dimensions, x, y, and z, but readers will recognize thatPoint To Point Adapters optimized for point-to-point operations in aparallel computer that manages internode data communications for anuninitialized process according to embodiments of the present inventionmay in fact be implemented so as to support communications in twodimensions, four dimensions, five dimensions, and so on. Severalsupercomputers now use five dimensional mesh or torus networks,including, for example, IBM's Blue Gene Q™.

For further explanation, FIG. 3B illustrates an example of a CollectiveOperations Adapter (188) useful in a parallel computer that managesinternode data communications for an uninitialized process according toembodiments of the present invention. Collective Operations Adapter(188) is designed for use in a network optimized for collectiveoperations, a network that organizes compute nodes of a parallelcomputer in a binary tree. Collective Operations Adapter (188) in theexample of FIG. 3B provides data communication to and from two childrennodes through four unidirectional data communications links (190).Collective Operations Adapter (188) also provides data communication toand from a parent node through two unidirectional data communicationslinks (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in parallel computers that manageinternode data communications for an uninitialized process according toembodiments of the present invention. In the example of FIG. 4, dotsrepresent compute nodes (102) of a parallel computer, and the dottedlines between the dots represent data communications links (103) betweencompute nodes. The data communications links are implemented withpoint-to-point data communications adapters similar to the oneillustrated for example in FIG. 3A, with data communications links onthree axis, x, y, and z, and to and fro in six directions +x (181), −x(182), +y (183), −y (184), +z (185), and −z (186). The links and computenodes are organized by this data communications network optimized forpoint-to-point operations into a three dimensional mesh (105). The mesh(105) has wrap-around links on each axis that connect the outermostcompute nodes in the mesh (105) on opposite sides of the mesh (105).These wrap-around links form a torus (107). Each compute node in thetorus has a location in the torus that is uniquely specified by a set ofx, y, z coordinates. Readers will note that the wrap-around links in they and z directions have been omitted for clarity, but are configured ina similar manner to the wrap-around link illustrated in the x direction.For clarity of explanation, the data communications network of FIG. 4 isillustrated with only 27 compute nodes, but readers will recognize thata data communications network optimized for point-to-point operations ina parallel computer that manages internode data communications for anuninitialized process according to embodiments of the present inventionmay contain only a few compute nodes or may contain thousands of computenodes. For ease of explanation, the data communications network of FIG.4 is illustrated with only three dimensions: x, y, and z, but readerswill recognize that a data communications network optimized forpoint-to-point operations may in fact be implemented in two dimensions,four dimensions, five dimensions, and so on. As mentioned, severalsupercomputers now use five dimensional mesh or torus networks,including IBM's Blue Gene Q™.

For further explanation, FIG. 5 illustrates an example datacommunications network (106) optimized for collective operations byorganizing compute nodes in a tree. The example data communicationsnetwork of FIG. 5 includes data communications links connected to thecompute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with collective operations data communicationsadapters similar to the one illustrated for example in FIG. 3B, witheach node typically providing data communications to and from twochildren nodes and data communications to and from a parent node, withsome exceptions. Nodes in a binary tree may be characterized as a rootnode (202), branch nodes (204), and leaf nodes (206). The root node(202) has two children but no parent. The leaf nodes (206) each has aparent, but leaf nodes have no children. The branch nodes (204) each hasboth a parent and two children. The links and compute nodes are therebyorganized by this data communications network optimized for collectiveoperations into a binary tree (106). For clarity of explanation, thedata communications network of FIG. 5 is illustrated with only 31compute nodes, but readers will recognize that a data communicationsnetwork optimized for collective operations for use in parallelcomputers that manage internode data communications for an uninitializedprocess according to embodiments of the present invention may containonly a few compute nodes or hundreds or thousands of compute nodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (196). The rank actually identifiesan instance of a parallel application that is executing on a computenode. That is, the rank is an application-level identifier. Using therank to identify a node assumes that only one such instance of anapplication is executing on each node. A compute node can, however,support multiple processors, each of which can support multipleprocessing cores—so that more than one process or instance of anapplication can easily be present under execution on any given computenode—or in all the compute nodes, for that matter. To the extent thatmore than one instance of an application executes on a single computenode, the rank identifies the instance of the application as such ratherthan the compute node. A rank uniquely identifies an application'slocation in the tree network for use in both point-to-point andcollective operations in the tree network. The ranks in this example areassigned as integers beginning with ‘0’ assigned to the root instance orroot node (202), ‘1’ assigned to the first node in the second layer ofthe tree, ‘2’ assigned to the second node in the second layer of thetree, ‘3’ assigned to the first node in the third layer of the tree, ‘4’assigned to the second node in the third layer of the tree, and so on.For ease of illustration, only the ranks of the first three layers ofthe tree are shown here, but all compute nodes, or rather allapplication instances, in the tree network are assigned a unique rank.Such rank values can also be assigned as identifiers of applicationinstances as organized in a mesh or torus network.

For further explanation, FIG. 6 sets forth a block diagram of an exampleprotocol stack useful in parallel computers that manage internode datacommunications for an uninitialized process according to embodiments ofthe present invention. The example protocol stack of FIG. 6 includes ahardware layer (214), a system messaging layer (212), an applicationmessaging layer (210), and an application layer (208). For ease ofexplanation, the protocol layers in the example stack of FIG. 6 areshown connecting an origin compute node (222) and a target compute node(224), although it is worthwhile to point out that in embodiments thateffect DMA data transfers, the origin compute node and the targetcompute node can be the same compute node. The granularity of connectionthrough the system messaging layer (212), which is implemented with aPAMI (218), is finer than merely compute node to compute node—because,again, communications among endpoints often is communications amongendpoints on the same compute node. For further explanation, recall thatthe PAMI (218) connfects endpoints, connections specified bycombinations of clients, contexts, and tasks, each such combinationbeing specific to a thread of execution on a compute node, with eachcompute node capable of supporting many threads and therefore manyendpoints. Every endpoint typically can function as both an originendpoint or a target endpoint for data transfers through a PAMI, andboth the origin endpoint and its target endpoint can be located on thesame compute node. So an origin compute node (222) and its targetcompute node (224) can in fact, and often will, be the same computenode.

The application layer (208) provides communications among instances of aparallel application (158) running on the compute nodes (222, 224) byinvoking functions in an application messaging module (216) installed oneach compute node. Communications among instances of the applicationthrough messages passed between the instances of the application.Applications may communicate messages invoking function of anapplication programming interface (‘API’) exposed by the applicationmessaging module (216). In this approach, the application messagingmodule (216) exposes a traditional interface, such as an API of an MPIlibrary, to the application program (158) so that the applicationprogram can gain the benefits of a PAMI, reduced network traffic,callback functions, and so on, with no need to recode the application.Alternatively, if the parallel application is programmed to use PAMIfunctions, the application can call the PAMI functions directly, withoutgoing through the application messaging module.

The example protocol stack of FIG. 6 includes a system messaging layer(212) implemented here as a PAMI (218). The PAMI provides system-leveldata communications functions that support messaging in the applicationlayer (602) and the application messaging layer (610). Such system-levelfunctions are typically invoked through an API exposed to theapplication messaging modules (216) in the application messaging layer(210). Although developers can in fact access a PAMI API directly bycoding an application to do so, a PAMI's system-level functions in thesystem messaging layer (212) in many embodiments are isolated from theapplication layer (208) by the application messaging layer (210), makingthe application layer somewhat independent of system specific details.With an application messaging module presenting a standard MPI API to anapplication, for example, with the application messaging module retooledto use the PAMI to carry out the low-level messaging functions, theapplication gains the benefits of a PAMI with no need to incur theexpense of reprogramming the application to call the PAMI directly.Because, however, some applications will in fact be reprogrammed to callthe PAMI directly, all entities in the protocol stack above the PAMI areviewed by PAMI as applications. When PAMI functions are invoked byentities above the PAMI in the stack, the PAMI makes no distinctionwhether the caller is in the application layer or the applicationmessaging layer, no distinction whether the caller is an application assuch or an MPI library function invoked by an application. As far as thePAMI is concerned, any caller of a PAMI function is an application.

The protocol stack of FIG. 6 includes a hardware layer (214) thatdefines the physical implementation and the electrical implementation ofaspects of the hardware on the compute nodes such as the bus, networkcabling, connector types, physical data rates, data transmissionencoding and many other factors for communications between the computenodes (222) on the physical network medium. In parallel computers thatmanage internode data communications for an uninitialized processaccording to embodiments of the present invention, the hardware layerincludes DMA controllers and network links, including routers, packetswitches, and the like.

For further explanation, FIG. 7 sets forth a functional block diagram ofan example PAMI (218) for use in parallel computers that manageinternode data communications for an uninitialized process according toembodiments of the present invention. The PAMI (218) provides an activemessaging layer that supports both point to point communications in amesh or torus as well as collective operations, gathers, reductions,barriers, and the like in tree networks, for example. The PAMI is amultithreaded parallel communications engine designed to provide lowlevel message passing functions, many of which are one-sided, andabstract such functions for higher level messaging middleware, referredto in this specification as ‘application messaging modules’ in anapplication messaging layer. In the example of FIG. 7, the applicationmessaging layer is represented by a generic MPI module (258),appropriate for ease of explanation because some form of MPI is a defacto standard for such messaging middleware. Compute nodes andcommunications endpoints of a parallel computer (102 on FIG. 1) arecoupled for data communications through such a PAMI and through datacommunications resources (294, 296, 314) that include DMA controllers,network adapters, and data communications networks through whichcontrollers and adapters deliver data communications. The PAMI (218)provides data communications among data communications endpoints, whereeach endpoint is specified by data communications parameters for athread of execution on a compute node, including specifications of aclient, a context, and a task.

The PAMI (218) in this example includes PAMI clients (302, 304), tasks(286, 298), contexts (290, 292, 310, 312), and endpoints (288, 300). APAMI client is a collection of data communications resources (294, 296,314) dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. Data communications resources assigned incollections to PAMI clients are explained in more detail below withreference to FIGS. 8A and 8B. PAMI clients (302, 304 on FIG. 7) enablehigher level middleware, application messaging modules, MPI libraries,and the like, to be developed independently so that each can be usedconcurrently by an application. Although the application messaging layerin FIG. 7 is represented for example by a single generic MPI module(258), in fact, a PAMI, operating multiple clients, can support multiplemessage passing libraries or application messaging modulessimultaneously, a fact that is explained in more detail with referenceto FIG. 9. FIG. 9 sets forth a functional block diagram of an examplePAMI (218) useful in parallel computers that manage internode datacommunications for an uninitialized process according to embodiments ofthe present invention in which the example PAMI operates, on behalf ofan application (158), with multiple application messaging modules(502-510) simultaneously. The application (158) can have multiplemessages in transit simultaneously through each of the applicationmessaging modules (502-510). Each context (512-520) carries out, throughpost and advance functions, data communications for the application ondata communications resources in the exclusive possession, in eachclient, of that context. Each context carries out data communicationsoperations independently and in parallel with other contexts in the sameor other clients. In the example FIG. 9, each client (532-540) includesa collection of data communications resources (522-530) dedicated to theexclusive use of an application-level data processing entity, one of theapplication messaging modules (502-510):

-   -   IBM MPI Library (502) operates through context (512) data        communications resources (522) dedicated to the use of PAMI        client (532),    -   MPICH Library (504) operates through context (514) data        communications resources (524) dedicated to the use of PAMI        client (534),    -   Unified Parallel C (‘UPC’) Library (506) operates through        context (516) data communications resources (526) dedicated to        the use of PAMI client (536),    -   Partitioned Global Access Space (‘PGAS’) Runtime Library (508)        operates through context (518) data communications resources        (528) dedicated to the use of PAMI client (538), and    -   Aggregate Remote Memory Copy Interface (‘ARMCI’) Library (510)        operates through context (520) data communications resources        (530) dedicated to the use of PAMI client (540).

Again referring to the example of FIG. 7: The PAMI (218) includes tasks,listed in task lists (286, 298) and identified (250) to the application(158). A ‘task’ as the term is used in PAMI operations is aplatform-defined integer datatype that identifies a canonicalapplication process, an instance of a parallel application (158). Verycarefully in this specification, the term ‘task’ is always used to referonly to this PAMI structure, not the traditional use of the computerterm ‘task’ to refer to a process or thread of execution. In thisspecification, the term ‘process’ refers to a canonical data processingprocess, a container for threads in a multithreading environment. Inparticular in the example of FIG. 7, the application (158) isimplemented as a canonical process with multiple threads (251-254)assigned various duties by a leading thread (251) which itself executesan instance of a parallel application program. Each instance of aparallel application is assigned a task; each task so assigned can be aninteger value, for example, in a C environment, or a separate taskobject in a C++ or Java environment. The tasks are components ofcommunications endpoints, but are not themselves communicationsendpoints; tasks are not addressed directly for data communications inPAMI. This gives a finer grained control than was available in priormessage passing art. Each client has its own list (286, 298) of tasksfor which its contexts provide services; this allows each process topotentially reside simultaneously in two or more differentcommunications domains as will be the case in certain advanced computersusing, for example, one type of processor and network in one domain anda completely different processor type and network in another domain, allin the same computer.

The PAMI (218) includes contexts (290, 292, 310, 312). A ‘context’ asthe term is used in PAMI operations is composed of a subset of aclient's collection of data processing resources, context functions, anda work queue of data transfer instructions to be performed by use of thesubset through the context functions operated by an assigned thread ofexecution. That is, a context represents a partition of the local datacommunications resources assigned to a PAMI client. Every context withina client has equivalent functionality and semantics. Context functionsimplement contexts as threading points that applications use to optimizeconcurrent communications. Communications initiated by a local process,an instance of a parallel application, uses a context object to identifythe specific threading point that will be used to issue a particularcommunication independent of communications occurring in other contexts.In the example of FIG. 7, where the application (158) and theapplication messaging module (258) are both implemented as canonicalprocesses with multiple threads of execution, each has assigned ormapped particular threads (253, 254, 262, 264) to advance (268, 270,276, 278) work on the contexts (290, 292, 310, 312), including executionof local callbacks (272, 280). In particular, the local event callbackfunctions (272, 280) associated with any particular communication areinvoked by the thread advancing the context that was used to initiatethe communication operation in the first place. Like PAMI tasks,contexts are not used to directly address a communication destination ortarget, as they are a local resource.

Context functions, explained here with regard to references (472-482) onFIG. 9, include functions to create (472) and destroy (474) contexts,functions to lock (476) and unlock (478) access to a context, andfunctions to post (480) and advance (482) work in a context. For ease ofexplanation, the context functions (472-482) are illustrated in only oneexpanded context (512); readers will understand, however, that all PAMIcontexts have similar context functions. The create (472) and destroy(474) functions are, in an object-oriented sense, constructors anddestructors. In the example embodiments described in thisspecifications, post (480) and advance (482) functions on a context arecritical sections, not thread safe. Applications using suchnon-reentrant functions must somehow ensure that critical sections areprotected from re-entrant use. Applications can use mutual exclusionlocks to protect critical sections. The lock (476) and unlock (478)functions in the example of FIG. 9 provide and operate such a mutualexclusion lock to protect the critical sections in the post (480) andadvance (482) functions. If only a single thread posts or advances workon a context, then that thread need never lock that context. To theextent that progress is driven independently on a context by a singlethread of execution, then no mutual exclusion locking of the contextitself is required—provided that no other thread ever attempts to call afunction on such a context. If more than one thread will post or advancework on a context, each such thread must secure a lock before calling apost or an advance function on that context. This is one reason why itis probably a preferred architecture, given sufficient resources, toassign one thread to operate each context. Progress can be driven withadvance (482) functions concurrently among multiple contexts by usingmultiple threads, as desired by an application—shown in the example ofFIG. 7 by threads (253, 254, 262, 264) which advance work concurrently,independently and in parallel, on contexts (290, 292, 310, 312).

Posts and advances (480, 482 on FIG. 9) are functions called on acontext, either in a C-type function with a context ID as a parameter,or in object oriented practice where the calling entity possesses areference to a context or a context object as such and the posts andadvances are member methods of a context object. Again referring to FIG.7: Application-level entities, application programs (158) andapplication messaging modules (258), post (266, 274) data communicationsinstructions, including SENDs, RECEIVEs, PUTs, GETs, and so on, to thework queues (282, 284, 306, 308) in contexts and then call advancefunctions (268, 270, 276, 278) on the contexts to progress specific dataprocessing and data communications that carry out the instructions. Thedata processing and data communications effected by the advancefunctions include specific messages, request to send (‘RTS’) messages,acknowledgments, callback execution, transfers of transfer data orpayload data, and so on. Advance functions therefore operate generallyby checking a work queue for any new instructions that need to beinitiated and checking data communications resources for any incomingmessage traffic that needs to be administered as well as increases instorage space available for outgoing message traffic, with callbacks andthe like. Advance functions also carry out or trigger transfers oftransfer data or payload data.

In at least some embodiments, a context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context.In the example of FIG. 7, context (290) has a subset (294) of a client's(302) data processing resources dedicated to the exclusive use of thecontext (290), and context (292) has a subset (296) of a client's (302)data processing resources dedicated to the exclusive use of the context(292). Advance functions (268, 270) called on contexts (290, 292)therefore never need to secure a lock on a data communications resourcebefore progressing work on a context—because each context (290, 292) hasexclusive use of dedicated data communications resources. Usage of datacommunications resources in this example PAMI (218), however, is notthread-safe. When data communications resources are shared amongcontexts, mutual exclusion locks are needed. In contrast to theexclusive usage of resources by contexts (290, 292), contexts (310, 312)share access to their client's data communications resource (314) andtherefore do not have data communications resources dedicated toexclusive use of a single context. Contexts (310, 312) therefore alwaysmust secure a mutual exclusion lock on a data communications resourcebefore using the resource to send or receive administrative messages ortransfer data.

For further explanation, here is an example pseudocode Hello Worldprogram for an application using a PAMI:

int main(int argc, char ** argv) { PAMI_client_t client; PAMI_context_tcontext; PAMI_result_t status = PAMI_ERROR; const char *name = “PAMI”;status = PAMI_Client_initialize(name, &client); size_t_n = 1; status =PAMI_Context_createv(client, NULL, 0, &context, _n);PAMI_configuration_t configuration; configuration.name = PAMI_TASK_ID;status = PAMI_Configuration_query(client, &configuration); size_ttask_id = configuration.value.intval; configuration.name =PAMI_NUM_TASKS; status = PAMI_Configuration_query(client,&configuration); size_t num_tasks = configuration.value.intval; fprintf(stderr, “Hello process %d of %d\n”, task_id, num_tasks); status =PAMI_Context_destroy(context); status = PAMI_Client_finalize(client);return 0; }

This short program is termed ‘pseudocode’ because it is an explanationin the form of computer code, not a working model, not an actual programfor execution. In this pseudocode example, an application initializes aclient and a context for an application named “PAMI.”PAMI_Client_initialize and PAMI_Context_createv are initializationfunctions (316) exposed to applications as part of a PAMI's API. Thesefunctions, in dependence upon the application name “PAMI,” pull from aPAMI configuration (318) the information needed to establish a clientand a context for the application. The application uses this segment:

PAMI_configuration_t configuration; configuration.name = PAMI_TASK_ID;status = PAMI_Configuration_query(client, &configuration); size_ttask_id = configuration.value.intval;to retrieve its task ID and this segment:

configuration.name = PAMI_NUM_TASKS; status =PAMI_Configuration_query(client, &configuration); size_t num_tasks =configuration.value.intval;to retrieve the number of tasks presently configured to carry outparallel communications and process data communications event in thePAMI. The applications prints “Hello process task_id of num_tasks,”where task_id is the task ID of the subject instance of a parallelapplication, and num_tasks is the number of instances of the applicationexecuting in parallel on compute nodes. Finally, the applicationdestroys the context and terminates the client.

For further explanation of data communications resources assigned incollections to PAMI clients, FIG. 8A sets forth a block diagram ofexample data communications resources (220) useful in parallel computersthat manage internode data communications for an uninitialized processaccording to embodiments of the present invention. The datacommunications resources of FIG. 8A include a gigabit Ethernet adapter(238), an Infiniband adapter (240), a Fibre Channel adapter (242), a PCIExpress adapter (246), a collective operations network configured as atree (106), shared memory (227), DMA controllers (225, 226), and anetwork (108) configured as a point-to-point torus or mesh like thenetwork described above with reference to FIG. 4. A PAMI is configuredwith clients, each of which is in turn configured with certaincollections of such data communications resources—so that, for example,the PAMI client (302) in the PAMI (218) in the example of FIG. 7 canhave dedicated to its use a collection of data communications resourcescomposed of six segments (227) of shared memory, six Gigabit Ethernetadapters (238), and six Infiniband adapters (240). And the PAMI client(304) can have dedicated to its use six Fibre Channel adapters (242), aDMA controller (225), a torus network (108), and five segments (227) ofshared memory. And so on.

The DMA controllers (225, 226) in the example of FIG. 8A each isconfigured with DMA control logic in the form of a DMA engine (228,229), an injection FIFO buffer (230), and a receive FIFO buffer (232).The DMA engines (228, 229) can be implemented as hardware components,logic networks of a DMA controller, in firmware, as software operatingan embedded controller, as various combinations of software, firmware,or hardware, and so on. Each DMA engine (228, 229) operates on behalf ofendpoints to send and receive DMA transfer data through the network(108). The DMA engines (228, 229) operate the injection buffers (230,232) by processing first-in-first-out descriptors (234, 236) in thebuffers, hence the designation ‘injection FIFO’ and ‘receive FIFO.’

For further explanation, here is an example use case, a description ofthe overall operation of an example PUT DMA transfer using the DMAcontrollers (225, 226) and network (108) in the example of FIG. 8A: Anoriginating application (158), which is typically one instance of aparallel application running on a compute node, places a quantity oftransfer data (494) at a location in its RAM (155). The application(158) then calls a post function (480) on a context (512) of an originendpoint (352), posting a PUT instruction (390) into a work queue (282)of the context (512); the PUT instruction (390) specifies a targetendpoint (354) to which the transfer data is to be sent as well assource and destination memory locations. The application then calls anadvance function (482) on the context (512). The advance function (482)finds the new PUT instruction in its work queue (282) and inserts a datadescriptor (234) into the injection FIFO of the origin DMA controller(225); the data descriptor includes the source and destination memorylocations and the specification of the target endpoint. The origin DMAengine (225) then transfers the data descriptor (234) as well as thetransfer data (494) through the network (108) to the DMA controller(226) on the target side of the transaction. The target DMA engine(229), upon receiving the data descriptor and the transfer data, placesthe transfer data (494) into the RAM (156) of the target application atthe location specified in the data descriptor and inserts into thetarget DMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) or application instancecalls an advance function (483) on a context (513) of the targetendpoint (354). The advance function (483) checks the communicationsresources assigned to its context (513) for incoming messages, includingchecking the receive FIFO (232) of the target DMA controller (226) fordata descriptors that specify the target endpoint (354). The advancefunction (483) finds the data descriptor for the PUT transfer andadvises the target application (159) that its transfer data has arrived.A GET-type DMA transfer works in a similar manner, with somedifferences, including, of course, the fact that transfer data flows inthe opposite direction. Similarly, typical SEND transfers also operatesimilarly, some with rendezvous protocols, some with eager protocols,with data transmitted in packets over the a network through non-DMAnetwork adapters or through DMA controllers.

The example of FIG. 8A includes two DMA controllers (225, 226). DMAtransfers between endpoints on separate compute nodes use two DMAcontrollers, one on each compute node. Compute nodes can be implementedwith multiple DMA controllers so that many or even all DMA transferseven among endpoints on a same compute node can be carried out using twoDMA engines. In some embodiments at least, however, a compute node, likethe example compute node (152) of FIG. 2, has only one DMA engine, sothat that DMA engine can be use to conduct both sides of transfersbetween endpoints on that compute node. For further explanation of thisfact, FIG. 8B sets forth a functional block diagram of an example DMAcontroller (225) operatively coupled to a network (108)—in anarchitecture where this DMA controller (225) is the only DMA controlleron a compute node—and an origin endpoint (352) and its target endpoint(354) are both located on the same compute node (152). In the example ofFIG. 8B, a single DMA engine (228) operates with two threads ofexecution (598, 599) on behalf of endpoints (352, 354) on a same computenode to send and receive DMA transfer data through a segment (227) ofshared memory. A transmit thread (598) injects transfer data into thenetwork (108) as specified in data descriptors (234) in an injectionFIFO buffer (230), and a receive thread (598) receives transfer datafrom the network (108) as specified in data descriptors (236) in areceive FIFO buffer (232).

The overall operation of an example PUT DMA transfer with the DMAcontrollers (225) and the network (108) in the example of FIG. 8B is: Anoriginating application (158), that is actually one of multipleinstances (158, 159) of a parallel application running on a compute node(152) in separate threads of execution, places a quantity of transferdata (494) at a location in its RAM (155). The application (158) thencalls a post function (480) on a context (512) of an origin endpoint(352), posting a PUT instruction (390) into a work queue (282) of thecontext (512); the PUT instruction specifies a target endpoint (354) towhich the transfer data is to be sent as well as source and destinationmemory locations. The application (158) then calls an advance function(482) on the context (512). The advance function (482) finds the new PUTinstruction (390) in its work queue (282) and inserts a data descriptor(234) into the injection FIFO of the DMA controller (225); the datadescriptor includes the source and destination memory locations and thespecification of the target endpoint. The DMA engine (228) thentransfers by its transmit and receive threads (598, 599) through thenetwork (108) the data descriptor (234) as well as the transfer data(494). The DMA engine (228), upon receiving by its receive thread (599)the data descriptor and the transfer data, places the transfer data(494) into the RAM (156) of the target application and inserts into theDMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) calls an advancefunction (483) on a context (513) of the target endpoint (354). Theadvance function (483) checks the communications resources assigned toits context for incoming messages, including checking the receive FIFO(232) of the DMA controller (225) for data descriptors that specify thetarget endpoint (354). The advance function (483) finds the datadescriptor for the PUT transfer and advises the target application (159)that its transfer data has arrived. Again, a GET-type DMA transfer worksin a similar manner, with some differences, including, of course, thefact that transfer data flows in the opposite direction. And typicalSEND transfers also operate similarly, some with rendezvous protocols,some with eager protocols, with data transmitted in packets over the anetwork through non-DMA network adapters or through DMA controllers.

By use of an architecture like that illustrated and described withreference to FIG. 8B, a parallel application or an application messagingmodule that is already programmed to use DMA transfers can gain thebenefit of the speed of DMA data transfers among endpoints on the samecompute node with no need to reprogram the applications or theapplication messaging modules to use the network in other modes. In thisway, an application or an application messaging module, alreadyprogrammed for DMA, can use the same DMA calls through a same API forDMA regardless whether subject endpoints are on the same compute node oron separate compute nodes.

For further explanation, FIG. 10 sets forth a functional block diagramof example endpoints useful in parallel computers that manage internodedata communications for an uninitialized process according toembodiments of the present invention. In the example of FIG. 10, a PAMI(218) is implemented with instances on two separate compute nodes (152,153) that include four endpoints (338, 340, 342, 344). These endpointsare opaque objects used to address an origin or destination in a processand are constructed from a (client, task, context) tuple. Non-DMA SENDand RECEIVE instructions as well as DMA instructions such as PUT and GETaddress a destination by use of an endpoint object or endpointidentifier.

Each endpoint (338, 340, 342, 344) in the example of FIG. 10 is composedof a client (302, 303, 304, 305), a task (332, 333, 334, 336), and acontext (290, 292, 310, 312). Using a client a component in thespecification of an endpoint disambiguates the task and contextidentifiers, as these identifiers may be the same for multiple clients.A task is used as a component in the specification of an endpoint toconstruct an endpoint to address a process accessible through a context.A context in the specification of an endpoint identifies, refers to, orrepresents the specific context associated with a destination or targettask—because the context identifies a specific threading point on atask. A context offset identifies which threading point is to process aparticular communications operation. Endpoints enable “crosstalk” whichis the act of issuing communication on a local context with a particularcontext offset that is directed to a destination endpoint with nocorrespondence to a source context or source context offset.

For efficient utilization of storage in an environment where multipletasks of a client reside on the same physical compute node, anapplication may choose to write an endpoint table (288, 300 on FIG. 7)in a segment of shared memory (227, 346, 348). It is the responsibilityof the application to allocate such segments of shared memory andcoordinate the initialization and access of any data structures sharedbetween processes. This includes any endpoint objects which are createdby one process or instance of an application and read by anotherprocess.

Endpoints (342, 344) on compute node (153) serve respectively twoapplication instances (157, 159). The tasks (334, 336) in endpoints(342, 344) are different. The task (334) in endpoint (342) is identifiedby the task ID (249) of application (157), and the task (336) inendpoint (344) is identified by the task ID (257) of application (159).The clients (304, 305) in endpoints (342, 344) are different, separateclients. Client (304) in endpoint (342) associates data communicationsresources (e.g., 294, 296, 314 on FIG. 7) dedicated exclusively to theuse of application (157), while client (305) in endpoint (344)associates data communications resources dedicated exclusively to theuse of application (159). Contexts (310, 312) in endpoints (342, 344)are different, separate contexts. Context (310) in endpoint (342)operates on behalf of application (157) a subset of the datacommunications resources of client (304), and context (312) in endpoint(344) operates on behalf of application (159) a subset of the datacommunications resources of client (305).

Contrasted with the PAMIs (218) on compute node (153), the PAMI (218) oncompute node (152) serves only one instance of a parallel application(158) with two endpoints (338, 340). The tasks (332, 333) in endpoints(338, 340) are the same, because they both represent a same instance ofa same application (158); both tasks (332,333) therefore are identified,either with a same variable value, references to a same object, or thelike, by the task ID (250) of application (158). The clients (302, 303)in endpoints (338, 340) are optionally either different, separateclients or the same client. If they are different, each associates aseparate collection of data communications resources. If they are thesame, then each client (302, 303) in the PAMI (218) on compute node(152) associates a same set of data communications resources and isidentified with a same value, object reference, or the like. Contexts(290, 292) in endpoints (338, 340) are different, separate contexts.Context (290) in endpoint (338) operates on behalf of application (158)a subset of the data communications resources of client (302) regardlesswhether clients (302, 303) are the same client or different clients, andcontext (292) in endpoint (340) operates on behalf of application (158)a subset of the data communications resources of client (303) regardlesswhether clients (302, 303) are the same client or different clients.Thus the tasks (332, 333) are the same; the clients (302, 303) can bethe same; and the endpoints (338, 340) are distinguished at least bydifferent contexts (290, 292), each of which operates on behalf of oneof the threads (251-254) of application (158), identified typically by acontext offset or a threading point.

Endpoints (338, 340) being as they are on the same compute node (152)can effect DMA data transfers between endpoints (338, 340) through DMAcontroller (225) and a segment of shared local memory (227). In theabsence of such shared memory (227), endpoints (338, 340) can effect DMAdata transfers through the DMA controller (225) and the network (108),even though both endpoints (338, 340) are on the same compute node(152). DMA transfers between endpoint (340) on compute node (152) andendpoint (344) on another compute node (153) go through DMA controllers(225, 226) and either a network (108) or a segment of shared remotememory (346). DMA transfers between endpoint (338) on compute node (152)and endpoint (342) on another compute node (153) also go through DMAcontrollers (225, 226) and either a network (108) or a segment of sharedremote memory (346). The segment of shared remote memory (346) is acomponent of a Non-Uniform Memory Access (‘NUMA’) architecture, asegment in a memory module installed anywhere in the architecture of aparallel computer except on a local compute node. The segment of sharedremote memory (346) is ‘remote’ in the sense that it is not installed ona local compute node. A local compute node is ‘local’ to the endpointslocated on that particular compute node. The segment of shared remotememory (346), therefore, is ‘remote’ with respect to endpoints (338,340) on compute node (158) if it is in a memory module on compute node(153) or anywhere else in the same parallel computer except on computenode (158).

Endpoints (342, 344) being as they are on the same compute node (153)can effect DMA data transfers between endpoints (342, 344) through DMAcontroller (226) and a segment of shared local memory (348). In theabsence of such shared memory (348), endpoints (342, 344) can effect DMAdata transfers through the DMA controller (226) and the network (108),even though both endpoints (342, 344) are on the same compute node(153). DMA transfers between endpoint (344) on compute node (153) andendpoint (340) on another compute node (152) go through DMA controllers(226, 225) and either a network (108) or a segment of shared remotememory (346). DMA transfers between endpoint (342) on compute node (153)and endpoint (338) on another compute node (158) go through DMAcontrollers (226, 225) and either a network (108) or a segment of sharedremote memory (346). Again, the segment of shared remote memory (346) is‘remote’ with respect to endpoints (342, 344) on compute node (153) ifit is in a memory module on compute node (158) or anywhere else in thesame parallel computer except on compute node (153).

For further explanation, FIG. 11 sets forth a flow chart illustrating anexample method of managing internode data communications for anuninitialized process in a parallel computer according to embodiments ofthe present invention. The method of FIG. 11 is carried out in aparallel computer similar to the example parallel computer (100) ofFIG. 1. Such a parallel computer (100) includes a plurality of computenodes. Each compute node is configured to execute a plurality ofprocesses. Each compute node includes a messaging unit and main computermemory. Each MU may be implemented as a module of automated computingmachinery coupling compute nodes for data communications. Each MUincludes computer memory which in turn includes one or more MU messagebuffers. Each MU message buffer is associated with an uninitializedprocess on the compute node.

The method of FIG. 11 includes receiving (1102), by an MU of a computenode, one or more data communications messages in an MU message bufferassociated with an uninitialized process on the compute node. In themethod of FIG. 11, receiving (1102) data communications messages in anMU message buffer is carried out by: allocating (1106), at boot time(1104) of the compute node by the MU, one or more MU message buffers inthe MU's computer memory; receiving (1108), by the MU from a process onanother compute node, the one or more data communications messageintended for the uninitialized process; and storing (1110) the datacommunications message in the MU message buffer associated with theuninitialized process.

The method of FIG. 11 also includes determining (1112), by anapplication agent, that the MU message buffer associated with theuninitialized process is full prior to initialization of theuninitialized process. Determining that the MU message buffer is fullmay be carried out in various ways, some of which are described belowwith reference to FIG. 12.

Once the application agent determines (1112) that the MU message bufferis full, the method of FIG. 11 continues by establishing (1114), by theapplication agent, a temporary message buffer for the uninitializedprocess in main computer memory. Establishing a temporary message buffermay be carried out by a system-level call to allocate memory of aparticular size. An example of such a system-level call in a Unix-typesystem includes the ‘malloc’ system call. In some embodiments thetemporary message buffer may be allocated in a shared memory segment ofmain memory. In establishing the temporary message buffer theapplication may also establish a data structure that includes metadatadescribing the temporary message buffer. The metadata may be utilized bythe application agent to maintain multiple different temporary messagebuffers where each buffer is associated with a different uninitializedprocess having a full MU message buffer. Such metadata may, for example,include a process identifier, a pointer to the beginning of the messagebuffer, a pointer to the end of the last data communications messagestored in the message buffer (or the first available memory address inthe temporary message buffer), and so on as will occur to readers ofskill in the art.

The method of FIG. 11 also includes moving (1116), by the applicationagent, data communications messages from the MU message bufferassociated with the uninitialized process to the temporary messagebuffer in main computer memory. Moving data communications message fromthe MU message buffer to the temporary message buffer may be carried outin a variety of ways including, for example, one or more atomic copy anddelete operations in which a message is copied to the temporary messagebuffer then deleted from the MU message buffer.

Upon initialization of the uninitialized process, the method of FIG. 11continues by providing (1118), by the application agent to theinitialized process, a pointer to the temporary message buffer.Providing the pointer to the initialized process may be carried out invarious ways. For example, the application may be registered with theoperating system (or PAMI) to be notified when the uninitialized processis initialized. Alternatively, each process may be configured to‘check-in’ with the application agent upon initialization. That is, eachprocess, upon initialization, may ask the application agent whether atemporary message buffer has been established.

If so, the application agent may respond to the requesting process withthe pointer to the process's temporary message buffer. Readers of skillin the art will recognize that there may be many other implementationsin which the initialized process retrieves the pointer and each suchimplementation is well within the scope of the present invention.

The method of FIG. 11 also includes consuming (1120), by the initializedprocess in dependence upon the pointer, data communications messagesfrom the temporary message buffer prior to consuming data communicationsmessages stored in the MU message buffer. Consuming (1120), by theinitialized process in dependence upon the pointer, data communicationsmessages from the temporary message buffer may be carried out in variousways. For example, the initialized process may consume messages in thetemporary buffer by copying the temporary buffer into a permanent bufferaccessible only by the initialized process and flushing the temporarybuffer. Then, the process may copy messages from the MU message bufferto the permanent buffer, effectively concatenating the MU message buffercontents to the end of the temporary buffer contents. The process maythen flush the MU message buffer. This is but one way among manypossible ways to ‘consume’ messages. Readers of skill in the art willrecognize that, rather than copying the messages into a permanent bufferfor processing, the initialized process may process the message inplace—that is, while the messages are stored in the message buffers.

The method of FIG. 11 may be implemented in a parallel computer thatincludes a PAMI (218). In such a parallel computer, the compute nodesmay execute a parallel application, and the PAMI may include datacommunications endpoints, with each endpoint including a specificationof data communications parameters for a thread of execution on a computenode, including specifications of a client, a context, and a task. Insuch an embodiment the endpoints may be coupled for data communicationsthrough the PAMI. In the method of FIG. 11, the uninitialized process aswell as processes sending data communications messages to theuninitialized process may be implemented in this example embodiment asPAMI endpoints.

FIG. 12 sets forth a flow chart illustrating a further example method ofmanaging internode data communications for an uninitialized process in aparallel computer according to embodiments of the present invention. Themethod of FIG. 12 is similar to the method of FIG. 11 in that the methodof FIG. 12 is carried out in a parallel computer similar to the exampleparallel computer (100) of FIG. 1. Such a parallel computer (100)includes a plurality of compute nodes. Each compute node is configuredto execute a plurality of processes. Each compute node includes amessaging unit and main computer memory. Each MU may be implemented as amodule of automated computing machinery coupling compute nodes for datacommunications. Each MU includes computer memory which in turn includesone or more MU message buffers. Each MU message buffer is associatedwith an uninitialized process on the compute node. The method of FIG. 12is also similar to the method of FIG. 11 in that the method of FIG. 12includes: receiving (1102) data communications messages in an MU messagebuffer associated with an uninitialized process; determining (1112) thatthe MU message buffer is full; establishing (1114) a temporary messagebuffer; and moving (1116) data communications messages from the MUmessage buffer to the temporary message buffer.

The method of FIG. 12 differs from the method of FIG. 11, however, inthat in the method of FIG. 12, determining (1112) that the MU messagebuffer is full is carried out in one of several alternative methods. Onemethod of determining (1112) that the MU message buffer is full includesperiodically polling (1202) the messaging unit for informationdescribing the size of all data communications messages presently storedin the MU message buffer and determining (1204) whether the size of alldata communications messages presently stored in the MU message bufferexceeds a predetermined threshold size. Such ‘information’ may beimplemented in a variety of ways. For example, the messaging unit maymaintain a flag (one or more bits in a well-known memory address) foreach MU message buffer. The state of the flag—set or not—may indicatewhether the MU message buffer is full. In such an embodiment, theapplication agent may poll the messaging unit for information describingthe size of all data communications messages by retrieving the presentstate of the flag associated with the MU message buffer. In otherembodiments, the messaging unit may maintain a pointer to a beginningmemory address of the MU message buffer and a pointer to the last memoryaddress in which a data communications message was written in the MUmessage buffer. In this way, the difference in the pointers, the memorysegment between the pointers, indicates the size of all datacommunications messages stored in the MU message buffer. In such anembodiment, the application agent may poll the messaging unit forinformation describing the size of all messages in the MU message bufferby polling the messaging unit for the pointers, or the differencebetween the pointers. Readers of skill in the art will recognize thatthese are but two example embodiments of “information describing thesize of all data communications messages presently stored in the MUmessage buffer,” among many possible embodiments. Each such embodimentis well within the scope of the present invention.

If the size of all data communications messages presently stored in theMU message buffer is not greater than the threshold, the applicationagent may be configured to wait for a predefined amount of time beforeagain polling (1202) the messaging unit. If the size of all datacommunications messages presently stored in the MU message buffer isgreater than the threshold, the application agent determines that the MUmessage buffer associated with the uninitialized process is full.

Another method of determining (1112) that the MU message buffer is fullincludes receiving (1206) a notification from the messaging unit that MUmessage buffer is full. Rather than polling periodically, in thisembodiment, the messaging unit is configured to notify or interrupt theapplication agent upon filling an MU message buffer. Receiving anotification from the messaging unit may be carried out in various ways.For example, the messaging unit may be configured raise a hardware-levelinterrupt or store an application-level notification in a predefinedmemory location known to the application agent. The notification may betransferred to the application agent in-band—through application-leveldata communications—or out-of-band, through hardware level out-of-banddata communications links to a processor core.

Also in the method of FIG. 12, moving (1116), by the application agent,data communications messages from the MU message buffer associated withthe uninitialized process to the temporary message buffer in maincomputer memory includes periodically moving (1208), by the applicationagent, one or more messages from the MU message buffer to the temporarymessage buffer prior to initialization of the uninitialized process.That is, once the temporary message buffer is established, the messagingunit may continue to receive data communications messages intended forthe uninitialized process and store the message in the MU messagebuffer. The application agent may therefore be configured toperiodically move those messages from the MU message buffer to thetemporary buffer until the process is fully initialized. The applicationagent may move the data periodically at a predefined time interval oron-demand, when the MU message buffer fills. In this way, the fargreater amount of main memory may be used as overflow for the limitedspace in which the MU message buffer is allocated.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

What is claimed is:
 1. A method of managing internode datacommunications in a parallel computer, the parallel computer comprisinga plurality of compute nodes, each compute node comprising main computermemory and a messaging unit (MU), each MU comprising a module ofautomated computing machinery coupling the plurality of compute nodesfor data communications, each MU comprising the main computer memory,the main computer memory of the MU comprising one or more MU messagebuffers, each MU message buffer associated with an uninitialized processon one of the plurality of compute nodes, the method comprising:receiving, by the MU of the one of the plurality of compute nodes, oneor more data communications messages in one of the one or more MUmessage buffers associated with the uninitialized process on the one ofthe plurality of compute nodes; determining, by an application agent,that the one of the one or more MU message buffers associated with theuninitialized process is full prior to initialization of theuninitialized process; establishing, by the application agent, atemporary message buffer for the uninitialized process in the maincomputer memory; and moving, by the application agent, the one or moredata communications messages from the one of the one or more MU messagebuffers associated with the uninitialized process to the temporarymessage buffer in the main computer memory, wherein: the parallelcomputer comprises a parallel active messaging interface (‘PAMI’) andthe plurality of compute nodes execute a parallel application, the PAMIcomprises data communications endpoints, each data communicationsendpoint comprising a specification of data communications parametersfor a thread of execution on one of the plurality of compute nodes,including specifications of a client, a context, and a task, the datacommunications endpoints coupled for data communications through thePAMI; and the uninitialized process comprises one of the datacommunications endpoints; each client comprises a collection of datacommunications resources dedicated to exclusive use of anapplication-level data processing entity; each context comprises asubset of the collection of data processing resources of a client,context functions, and a work queue of data transfer instructions to beperformed by use of the subset through the context functions operated byan assigned thread of execution; and each task represents a process ofexecution of the parallel application.
 2. The method of claim 1 furthercomprising: upon initialization of the uninitialized process, providing,by the application agent to the initialized process, a pointer to thetemporary message buffer; and consuming, by the initialized process independence upon the pointer, the one or more data communicationsmessages from the temporary message buffer prior to consuming the one ormore data communications messages stored in the one of the one or moreMU message buffers associated with the uninitialized process.
 3. Themethod of claim 1 wherein the determining, by the application agent,that the one of the one or more MU message buffers associated with theuninitialized process is full prior to initialization of theuninitialized process further comprises: periodically polling the MU forinformation describing the size of all data communications messagespresently stored in the one of the one or more of the MU messagebuffers; and determining whether the size of all data communicationsmessages presently stored in the one of the one or more of the MUmessage buffers exceeds a predetermined threshold size.
 4. The method ofclaim 1 wherein determining, by the application agent, that the one ofthe one or more MU message buffers associated with the uninitializedprocess is full prior to initialization of the uninitialized processfurther comprises receiving a notification from the MU that the one ofthe one or more MU message buffers is full.
 5. The method of claim 1wherein the moving, by the application agent, the one or more datacommunications messages from the one of the one or more MU messagebuffers associated with the uninitialized process to the temporarymessage buffer in the main computer memory further comprises:periodically moving, by the application agent, one or more messages fromthe one of the one or more MU message buffer to the temporary messagebuffer prior to initialization of the uninitialized process.
 6. Themethod of claim 1 wherein each compute node comprises a multi-corecomputer processor and one core of the multi-core processor executesonly the application agent.
 7. The method of claim 1 wherein thereceiving, by the MU of the one of the plurality of compute nodes, oneor more data communications messages in one of the one or more MUmessage buffers associated with the uninitialized process on the one ofthe plurality of compute nodes, further comprises: allocating, at boottime of the one of the plurality of compute nodes by the MU, the one ofthe one or more MU message buffers in the main computer memory of theMU's; receiving, by the MU from a process on another one of theplurality of compute nodes, the one or more data communications messageintended for the uninitialized process; and storing the one or more datacommunications messages in the one of the one or more MU message buffersassociated with the uninitialized process.
 8. The method of claim 1wherein each context carries out, through post and advance functions,data communications for the parallel application on data communicationsresources in the exclusive possession of that context.
 9. The method ofclaim 1 wherein each context carries out data communications operationsindependently and in parallel with other contexts.